Analysing the potential of plant clinics to boost crop protection in Rwanda through adoption of IPM: the case of maize and maize stem borers
Bibliographic record
Abstract
Maize plays an important role in the livelihoods of rural communities in Rwanda. However, maize yields are threatened by the presence of pests and diseases and a general lack of knowledge and information for their management. In this study we sought to assess if plant clinics are making farmers more aware and knowledgeable of pests and diseases and are indirectly contributing to higher yields. We interviewed 644 farmers across Rwanda, both users and non-users of plant clinics. Propensity score matching was used to match the users and non-users of plant clinics and logistic regression was used to assess a number of factors, including interactions with plant clinics, that affect farmers’ adoption of pest management practices. Our analysis shows that users of plant clinics are more aware and knowledgeable in recognizing and handling maize stem borers. Furthermore, users of plant clinics have on average higher yields than non-users and this difference is highly significant (P < 0.001). The analysis therefore demonstrates that plant clinics are beneficial to farmers in Rwanda. However, efforts are required to diversify the spectrum of practices that are promoted by plant clinics and by extension and advisory services in Rwanda.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".